Further Discussion on the Error Bound for Generalized Linear Complementarity Problem over a Polyhedral Cone

In this paper, we consider the global error bound for the generalized linear complementarity problem over a polyhedral cone (GLCP). Based on the new transformation of the problem, we establish its global error bound under milder conditions, which improves the result obtained by Sun and Wang (2009) for GLCP by weakening the assumption.     

Generalized Polynomial Complementarity Problems over a Polyhedral Cone

Journal of Optimization Theory and Applications - The goal of this paper is to investigate a new model, called generalized polynomial complementarity problems over a polyhedral cone and denoted by...     

A Nonsmooth L-M Method for Solving the Generalized Nonlinear Complementarity Problem over a Polyhedral Cone

In this paper the generalized nonlinear complementarity problem (GNCP) defined on a polyhedral cone is reformulated as a system of nonsmooth equations. Based on this reformulation, the famous Levenberg-Marquardt (L-M) algorithm is employed to obtain its solution. Theoretical results that relate the stationary points of the merit function to the solution of the GNCP are presented. Under mild assumptions, we show that the L-M algorithm is both globally and superlinearly convergent. Moreover, a method to calculate a generalized Jacobian is given and numerical experimental results are presented.     

On Uniform Global Error Bounds for Convex Inequalities

This paper studies the existence of a uniform global error bound when a convex inequality g 0, where g is a closed proper convex function, is perturbed. The perturbation neighborhoods are defined by small arbitrary perturbations of the epigraph of its conjugate function. Under certain conditions, it is shown that for sufficiently small arbitrary perturbations the perturbed system is solvable and there exists a uniform global error bound if and only if g satisfies the Slater condition and the solution set is bounded or its recession function satisfies the Slater condition. The results are used to derive lower bounds on the distance to ill-posedness.     

Convergence and Error Bound for Perturbation of Linear Programs

In various penalty/smoothing approaches to solving a linear program, one regularizes the problem by adding to the linear cost function a separable nonlinear function multiplied by a small positive parameter. Popular choices of this nonlinear function include the quadratic function, the logarithm function, and the x ln(x)-entropy function. Furthermore, the solutions generated by such approaches may satisfy the linear constraints only inexactly and thus are optimal solutions of the regularized problem with a perturbed right-hand side. We give a general condition for such an optimal solution to converge to an optimal solution of the original problem as the perturbation parameter tends to zero. In the case where the nonlinear function is strictly convex, we further derive a local (error) bound on the distance from such an optimal solution to the limiting optimal solution of the original problem, expressed in terms of the perturbation parameter.     

约束最优化问题中投影梯度的全局误差界及其应用

文章利用序列二次规划(SQP)方法中的价值函数为约束最优化问题的投影梯度提供了一个全局误差界,并利用这个全局误差界给出了可行解点列具有收敛性的充分与必要条件.     

The Monotonicity of the Linear Complementarity Problem

The monotonicity of the linear complementarity problem (LCP) is discussed in this paper. Both the monotone property about the single element of the solution and the monotone property of the whole solution are presented. In order to illustrate the results, some corresponding numerical experiments are provided.     

Convergence and Error Bound of a Method for Solving Variational Inequality Problems via the Generalized D-Gap Function

The variational inequality problem (VIP) can be reformulated as an unconstrained minimization problem through the generalized D-gap function. Recently, a hybrid Newton-type method was proposed by Peng and Fukushima for minimizing a special form of the generalized D-gap function. In this paper, the hybrid Newton-type algorithm is extended to minimize the general form g of the generalized D-gap function. It is shown that the algorithm has nice convergence properties. Under some reasonable conditions, it is proved that the algorithm is locally and globally convergent. Moreover, it is proved that the function g has bounded level sets for strongly monotone VIP. An error bound of the algorithm is obtained.     

求线性比试和问题的确定性全局优化算法

为求线性比试和问题的全局最优解,本文给出了一个分支定界算法.通过一个等价问题和一个新的线性化松弛技巧,初始的非凸规划问题归结为一系列线性规划问题的求解.借助于这一系列线性规划问题的解,算法可收敛于初始非凸规划问题的最优解.算法的计算量主要是一些线性规划问题的求解.数值算例表明算法是切实可行的.     

A Note on an Error Bound for the AOR Method

Suppose Ax = b is a system of linear equations where the matrix A is symmetric positive definite and consistently ordered. A bound for the norm of the errors _k = x– x ^k of the AOR method in terms of the norms of _k = x ^k–x ^k–1 and _k+1 = x ^k+1–x ^k and their inner product is derived.     

The Global Classical Solution of the Initial Boundary Value Problem for Generalized KdV Equation

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一类新的非线性比式和问题的分枝定界算法(英文)

对一类新的非线性比式和问题(SNR)提出分枝定界算法,该问题的研究还很少.首先,通过两层线性化技术,构造一个松弛线性规划,求解该线性规划问题,得到问题(SNR)最优值的下界.其次,介绍新的下界更新技术,证明所给算法的收敛性.数值试验显示了算法的可行性和有效性     

Nonmonotone Inexact Newton Method for the Extended Linear Complementarity Problem

This paper presents a nonmonotone inexact Newton-type method for the extended linear complementarity problem (ELCP). We first reformulate the optimization system of the ELCP problem into a system of smoothed equations. Then we solve this system by a nonmonotone inexact Newton-type algorithm. The global convergence is obtained and numerical tests for some classes of ELCP include linear complementarity, horizontal linear complementarity, and generalized linear complementarity problems are also given to show the e?ciency of the proposed algorithm.     

Target-Oriented Branch and Bound Method for Global Optimization

We introduce a very simple but efficient idea for branch and bound (&) algorithms in global optimization (GO). As input for our generic algorithm, we need an upper bound algorithm for the GO maximization problem and a branching rule. The latter reduces the problem into several smaller subproblems of the same type. The new & approach delivers one global optimizer or, if stopped before finished, improved upper and lower bounds for the problem. Its main difference to commonly used & techniques is its ability to approximate the problem from above and from below while traversing the problem tree. It needs no supplementary information about the system optimized and does not consume more time than classical & techniques. Experimental results with the maximum clique problem illustrate the benefit of this new method.     

A Reduced Space Branch and Bound Algorithm for Global optimization

A general class of branch and bound algorithms forsolving a wide class of nonlinear programs with branching only in asubset of the problem variables is presented. By reducing the dimension of thesearch space, this technique may dramatically reduce the number ofiterations and time required for convergence to tolerancewhile retaining proven exact convergence in the infinite limit. Thispresentation includes specifications of the class of nonlinearprograms, a statement of a class of branch and bound algorithms, aconvergence proof, and motivating examples with results.     

Error Estimation for Nonlinear Complementarity Problems via Linear Systems with Interval Data

For the nonlinear complementarity problem, we derive norm bounds for the error of an approximate solution, generalizing the known results for the linear case. Furthermore, we present a linear system with interval data, whose solution set contains the error of an approximate solution. We perform extensive numerical tests and compare the different approaches.     

Interval Branch and Bound with Local Sampling for Constrained Global Optimization

In this article, we introduce a global optimization algorithm that integrates the basic idea of interval branch and bound, and new local sampling strategies along with an efficient data structure. Also included in the algorithm are procedures that handle constraints. The algorithm is shown to be able to find all the global optimal solutions under mild conditions. It can be used to solve various optimization problems. The local sampling (even if done stochastically) is used only to speed up the convergence and does not affect the fact that a complete search is done. Results on several examples of various dimensions ranging from 1 to 100 are also presented to illustrate numerical performance of the algorithm along with comparison with another interval method without the new local sampling and several noninterval methods. The new algorithm is seen as the best performer among those tested for solving multi-dimensional problems.     

B-矩阵线性互补问题的误差界估计

利用严格对角占优M-矩阵的逆矩阵的无穷大范数的范围,给出了B-矩阵线性互补问题误差界新的估计式.相应数值算例表明了结果的有效性.     

求广义线性比试和问题全局解的新方法

为确定广义线性比式和规划问题(GFP)的全局最优解,提出一个新的分支定界方法.在算法中,分支过程采用单纯形对分规则,且界的估计通过一些线性规划问题的求解完成.给出算法的收敛性证明.数值试验结果显示算法是有效可行的.     

一类广义层流方程初值问题的整体适定性

讨论了刻画层流问题中比重相近的层间相互作用的数学模型的初值问题.通过引进一类函数空间并证明该初值问题的解在所述空间上的一系列先验估计,得到了该初值问题在初值属于Hs(R)(s≥1)时的整体适定性.